The selection of elective courses based on an individual’s domain interest is a challenging and critical activity for students at the start of their curriculum. Effective and proper recommendation may result in building a strong expertise in the domain of interest, which in turn improves the outcomes of the students getting better placements, and enrolling into higher studies of their interest, etc. In this paper, an effective course recommendation system is proposed to help the students in facilitating proper course selection based on an individual’s domain interest. To achieve this, the core courses in the curriculum are mapped with the predefined domain suggested by the domain experts. These core course contents mapped with the domain are trained semantically using deep learning models to classify the elective courses into domains, and the same are recommended based on the student’s domain expertise. The recommendation is validated by analyzing the number of elective course credits completed and the grades scored by a student who utilized the elective course recommendation system, with the grades scored by the student who was subjected to the assessment without elective course recommendations. It was also observed that after the recommendation, the students have registered for a greater number of credits for elective courses on their domain of expertise, which in-turn enables them to have a better learning experience and improved course completion probability.

Software projects mostly exceeds budget, delivered late and does not meet with the customer’s satisfaction for years. In the past, many traditional development models like waterfall, spiral, iterative, and prototyping methods are used to build the software systems. In recent years, agile models are widely used in developing the software products. The major reasons are – simplicity, incorporating the requirement changes at any time, light-weight approach and delivering the working product early and in short duration. Whatever the development model used, it still remains a challenge for software engineer’s to accurately estimate the size, effort and the time required for developing the software system. This survey focuses on the existing estimation models used in traditional as well in agile software development.

The Semantic Web contains a large amount of data in the form of knowledge bases. Question Answering system is the most promising way of retrieving data from the available knowledge base to the end-users, to get the appropriate result for their questions. Although many systems have been developed over the years, it remains a challenge that most systems yet to require improvements to increase the accuracy for correct interpretation of the question and provide an answer. Many Question Answering systems convert the questions into triples which are mapped to the Knowledge base from which answer is derived. However, these triples do not express the semantic representation of the question, due to which the answers cannot be located. To handle this, a template-based approach is proposed which classifies the question types and finds appropriate SPARQL query templates for each type including comparatives and superlatives. The SPARQL query built is executed in the DBpedia endpoint and results are obtained. Compared with other factoid question answering systems, the proposed approach has the potential to deal with a large number of question types, including comparatives and superlatives. Also, the experimental evaluations of the system performed on the QALD-8 dataset present good performance and can help users to find answers to their questions.

Semantic Associations are complex relationships between entities over metadata represented in a RDF graph. While searching for complex relationships, it is possible to find too many relationships between entities. Therefore, it is important to locate interesting and meaningful relations and rank them before presenting to the end user. In recent years e-learning systems have become very popular in all fields of higher education. In an e-learning environment, user may expect to search the semantic relationship paths between two concepts or entities. There may be numerous relationships between two entities which involve more intermediate entities. In order to filter the size of results set based on user's relevance, user may introduce one or more known intermediate entities. In this paper, we present a Modified bidirectional Breadth-First-Search algorithm for finding paths between two entities which pass through other intermediate entities and the paths are ranked according to the users' needs. We have evaluated our system through empirical evaluation. We have compared the execution time to discover the paths between entities for our proposed search method and existing method. According to our experiments our proposed algorithm improves search efficiently. The average correlation coefficient between the proposed system ranking and the human ranking is 0.69. It explains that our proposed system ranking is highly correlated with human ranking.